Special Issue "Deep Reinforcement Learning for Multi-Agent Systems"
Deadline for manuscript submissions: 31 January 2024 | Viewed by 201
Interests: probabilistic verification; self-adaptive software systems; event-streaming software systems; autonomous agents; reinforcement learning; data mining
Deep reinforcement learning (DRL) have attracted numerous real-world applications in various domains. While the majority of DRL research is now focused on single agents, there is a fast-growing trend to extend DRL for problems in multiagent systems. Several foundational approaches to multiagent DRL (MADRL) have emerged, including counterfactual multi-agent (COMA), value-decomposition networks (VDNs) and QMIX, but there are still many challenges in this area, such as large-scale agent teams, heterogeneity and robustness.
This Special Issue aims to collect most up-to-date research in MADRL, ranging from theoretical problems to practical application, and to simulation platforms and applications in this area. Specific topics include (but are not limited to): scalability, partial observability, non-stationarity, credit assignment, heterogeneity, curriculum learning, mechanism design, dynamic programming, planning, communication, adversarial attachment, security, empirical study, simulation, and reflection on current trends.
Dr. Guoxin Su
Manuscript Submission Information
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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1200 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
- deep reinforcement learning
- multi-agent system